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February 6, 2025

Reimagining Your SOC: Unlocking a Proactive State of Security

Reimagining your SOC Part 3/3: This blog explores the challenges security professionals face in managing cyber risk, evaluates current market solutions, and outlines strategies for building a proactive security posture.
Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Gabriel Few-Wiegratz
Product Marketing Manager, Exposure Management and Incident Readiness
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06
Feb 2025

Part 1: How to Achieve Proactive Network Security

Part 2: Overcoming Alert Fatigue with AI-Led Investigations  

While the success of a SOC team is often measured through incident management effectiveness (E.g MTTD, MTTR), a true measure of maturity is the reduction of annual security incidents.

Organizations face an increasing number of alerts each year, yet the best SOC teams place focus on proactive operations which don’t reduce the threshold for what becomes an incident but targets the source risks that prevent them entirely.

Freeing up time to focus on cyber risk management is a challenge in and of itself, we cover this in the previous two blogs in this series (see above). However, when the time comes to manage risk, there are several challenges that are unique when compared to detection & response functions within cybersecurity.

Why do cyber risks matter?

While the volume of reported CVEs is increasing at an alarming rate[1], determining the criticality of each vulnerability is becoming increasingly challenging, especially when the likelihood and impact may be different for each organization. Yet vulnerabilities have stood as an important signpost in traditional security and mitigation strategies. Now, without clear prioritization, potentially severe risks may go unreported, leaving organizations exposed to significant threats.

Vulnerabilities also represent just one area of potential risks. Cyberattacks are no longer confined to a single technology type. They now traverse various platforms, including cloud services, email systems, and networks. As technology infrastructure continues to expand, so does the attack surface, making comprehensive visibility across all technology types essential for reducing risk and preventing multi-vector attacks.

However, achieving this visibility is increasingly difficult as infrastructure grows and the cyber risk market remains oversaturated. This visibility challenge extends beyond technology to include personnel and individual cyber hygiene which can still exacerbate broader cyberattacks whether malicious or not.

Organizations must adopt a holistic approach to preventative security. This includes improving visibility across all technology types, addressing human risks, and mobilizing swiftly against emerging security gaps.

“By 2026, 60% of cybersecurity functions will implement business-impact-focused risk assessment methods, aligning cybersecurity strategies with organizational objectives.” [2]

The costs of a fragmented approach

siloed preventative security measures or technologies
Figure 1: Organizations may have a combination of siloed preventative security measures or technologies in place

Unlike other security tools (like SIEM, NDR or SOAR) which contain an established set of capabilities, cyber risk reduction has not traditionally been defined by a single market, rather a variety of products and practices that each provide their own value and are overwhelming if too many are adopted. Just some examples include:

  • Threat and Vulnerability management: Leverages threat intelligence, CVEs and asset management; however, leaves teams with significant patching workflows, ignores business & human factors and is reliant on the speed of teams to keep up with each passing update.  
  • Continuous Controls Monitoring (CCM): Automatically audits the effectiveness of security controls based on industry frameworks but requires careful prioritization and human calculations to set-up effectively. Focuses solely on mobilization.
  • Breach and Attack Simulation (BAS): Automates security posture testing through mock scenarios but require previous prioritization and might not tell you how your specific technologies can be mitigated to reduce that risk.
  • Posture Management technologies: Siloed approaches across Cloud, SaaS, Data Security and even Gen AI that reactively assess misconfigurations and suggest improvements but with only industry frameworks to validate the importance of the risks.
  • Red teaming & Penetration testing: Required by several regulations including (GDPR, HIPPA, PCI, DSS), many organizations hire 'red teams' to perform real breaches in trusted conditions. Penetration tests reveal many flaws, but are not continuous, requiring third-party input and producing long to-do lists with input of broader business risk dependent on the cost of the service.
  • Third-party auditors: Organizations also use third-party auditors to identify assets with vulnerabilities, grade compliance, and recommend improvements. At best, these exercises become tick-box exercises for companies to stay in compliance with the responsibility still on the client to perform further discovery and actioning.

Many of these individual solutions on the market offer simple enhancement, or an automated version of an existing human security task. Ultimately, they lack an understanding of the most critical assets at your organization and are limited in scope, only working in a specific technology area or with the data you provide.

Even when these strategies are complete, implementation of the results require resources, coordination, and buy-in from IT, cybersecurity, and compliance departments. Given the nature of modern business structures, this can be labor and time intensive as responsibilities are shared by organizational segmentation spread across IT, governance, risk and compliance (GRC), and security teams.

Prioritize your true cyber risk with a CTEM approach

Organizations with robust security programs benefit from well-defined policies, standards, key risk indicators (KRIs), and operational metrics, making it easier to measure and report cyber risk accurately.

Implementing a framework like Gartner’s CTEM (Continuous Threat Exposure Management) can help governance by defining the most relevant risks to each organization and which specific solutions meet your improvement needs.

This five-step approach—scoping, discovery, prioritization, validation, and mobilization—encourages focused management cycles, better delegation of responsibilities and a firm emphasis on validating potential risks through technological methods like attack path modeling or breach and attack simulation to add credibility.

Implementing CTEM requires expertise and structure. This begins with an exposure management solution developed uniquely alongside a core threat detection and response offering, to provide visibility of an organization’s most critical risks, whilst linking directly to their incident-based workflows.

“By 2026, organizations prioritizing their security investments, based on a continuous threat exposure management program, will realize a two-third reduction in breaches.” [3]

Achieving a proactive security posture across the whole estate

Unlike conventional tools that focus on isolated risks, Darktrace / Proactive Exposure Management breaks down traditional barriers. Teams can define risk scopes with full, prioritized visibility of the critical risks between: IT/OT networks, email, Active Directory, cloud resources, operational groups, (or even the external attack surface by integrating with Darktrace / Attack Surface Management).

Our innovative, AI-led risk discovery provides a view that mirrors actual attacker methodologies. It does this through advanced algorithms that determine risk based on business importance, rather than traditional device-type prioritization. By implementing a sophisticated damage assessment methodology, security teams don’t just prioritize via severity but instead, the inherent impact, damage, weakness and external exposure of an asset or user.

These calculations also revolutionize vulnerability management by combining industry standard CVE measurements with that organization-specific context to ensure patch management efforts are efficient, rather than an endless list.

Darktrace also integrates MITRE ATT&CK framework mappings to connect all risks through attack path modeling. This offers validation to our AI’s scoring by presenting real world incident scenarios that could occur across your technologies, and the actionable mitigations to mobilize against them.

For those human choke points, security may also deploy targeted phishing engagements. These send real but harmless email ‘attacks’ to test employee susceptibility, strengthening your ability to identify weak points in your security posture, while informing broader governance strategies.

Combining risk with live detection and response

Together, each of these capabilities let teams take the best steps towards reducing risk and the volume of incidents they face. However, getting proactive also sharpens your ability to handle live threats if they occur.  

During real incidents Darktrace users can quickly evaluate the potential impact of affected assets, create their own risk detections based on internal policies, strengthen their autonomous response along critical attack paths, or even see the possible stage of the next attack.

By continually ingesting risk information into live triage workflows, security teams will develop a proactive-first mindset, prioritizing the assets and alerts that have the most impact to the business. This lets them utilize their resource in the most efficient way, freeing up even more time for risk management, mitigation and ensuring continuity for the business.

Whether your organization is laying the foundation for a cybersecurity program or enhancing an advanced one, Darktrace’s self-learning AI adapts to your needs:

  • Foundational stage: For organizations establishing visibility and automating detection and response.
  • Integrated stage: For teams expanding coverage across domains and consolidating tools for simplicity.
  • Proactive stage: For mature security programs enhancing posture with vulnerability management and risk prioritization.

The Darktrace ActiveAI Security Platform empowers security teams to adopt a preventative defense strategy by using Cyber AI Analyst and autonomous response to fuel quicker triage, incident handling and give time back for proactive efforts designed around business impact. The platform encapsulates the critical capabilities that help organizations be proactive and stay ahead of evolving threats.

darktrace proactive exposure management solution brief reduce risk cyber risk

Download the solution brief

Maximize security visibility and reduce risk:

  • Unify risk exposure across all technologies with AI-driven scoring for CVEs, human communications, and architectures.
  • Gain cost and ROI insights on CVE risks, breach costs, patch latency, and blind spots.
  • Strengthen employee awareness with targeted phishing simulations and training.
  • Align proactive and reactive security by assessing device compromises and prevention strategies.
  • Reduce risk with tailored guidance that delivers maximum impact with minimal effort.

Take control of your security posture today. Download here!

References

[1] https://nvd.nist.gov/vuln/search, Search all, Statistics, Total matches By Year 2023 against 2024

[2] https://www.gartner.com/en/documents/5598859

[3] https://www.gartner.com/en/articles/how-to-manage-cybersecurity-threats-not-episodes

Inside the SOC
Darktrace cyber analysts are world-class experts in threat intelligence, threat hunting and incident response, and provide 24/7 SOC support to thousands of Darktrace customers around the globe. Inside the SOC is exclusively authored by these experts, providing analysis of cyber incidents and threat trends, based on real-world experience in the field.
Written by
Gabriel Few-Wiegratz
Product Marketing Manager, Exposure Management and Incident Readiness

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May 21, 2026

Darktrace named a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) For the Second Consecutive Year

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Continued recognition in NDR  

Darktrace has been recognized as a Leader in the 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), marking the second consecutive year in the Leaders quadrant.

We believe this consistency reflects sustained ability to execute, adapt, and deliver outcomes as the market evolves.

While we are immensely proud to be recognized by industry analysts as a Leader in NDR, that's just part of the story. Darktrace was also Named the Only 2025 Gartner® Peer Insights™ Customers’ Choice for Network Detection and Response based on direct customer feedback and real-world experience.

We believe the combination of these two signals is important. One reflects how the market is evaluated. The other reflects how technology performs in practice.

Why Darktrace continues to be recognized as a leader

We believe our position as a Leader for the second consecutive year reflects a combination of our sustained ability to execute in NDR, continued AI innovation, and proven delivery of security outcomes for customers and partners worldwide.

We also feel that our leadership in the NDR market is a testament to our unique and multi-layered AI approach, for which we were recognized as No.7 on Fast Company’s Most Innovative AI Companies of 2026 list, plus one of the hottest AI cybersecurity companies in CRN's AI 100.

Adapting to complex, real-world environments

Organizations are no longer protecting a single network perimeter. They are securing a mix of users, devices, applications, and data that move across hybrid environments.

Darktrace has focused on maintaining visibility and detection across these conditions, allowing security teams to understand activity as it scales.

Supporting organizations globally, not just technically

Security outcomes are shaped as much by deployment and support as they are by detection capability.

Darktrace continues to invest in regional presence across 29 countries around the world, helping organizations operationalize NDR in ways that align with local requirements, internal processes, and team structures.

Continuing to push AI beyond detection

AI in cybersecurity is often positioned as a way to improve detection accuracy. But the more important shift is how AI can influence decision-making and response.

Darktrace continues to develop models that learn from both live environments and historical incident data, combining real-time behavioral analysis with insights derived from prior attack patterns.

Using technologies such as the Incident Graph and DIGEST (Darktrace Incident Graph Evaluation for Security Threats), activity is not analyzed in isolation. Instead, relationships between users, devices, connections, and events are mapped over time, allowing the system to reconstruct how an incident is unfolding and how similar incidents have progressed in the past.

By evaluating these patterns, Darktrace can assess the likelihood that an incident will escalate, prioritizing the activity that poses the greatest risk and surfacing the most relevant context for investigation.

This shifts security operations from simply identifying anomalies to understanding their trajectory, helping teams anticipate potential impact and respond earlier with greater precision.

Why NDR is shifting from reactive detection to proactive, AI-driven security

Traditional approaches to NDR have been built around reactively identifying threats once they become clearly visible. That model is increasingly difficult to rely on.

Attackers are no longer operating in ways that stand out. They use valid credentials, trusted tools, and low-and-slow techniques that blend into everyday activity. By the time something looks obviously malicious, the impact is often already underway.

This is the core limitation of reactive detection. It depends on recognizing something that already looks like a threat.

As a result, many of the most consequential incidents today fall into a gap.

Insider activity, compromised credentials, and novel attacks rarely trigger traditional alerts because they do not follow known patterns. On the surface, they often appear legitimate, making them difficult to distinguish from normal behavior without deeper context.

This is why we believe this Gartner recognition reflects a broader shift in NDR toward autonomous, proactive and pre‑emptive security operations.

By understanding normal behavior within an environment, it is possible to identify subtle deviations rather than waiting for confirmation of threats as they are taking place.

Darktrace’s Self-Learning AI is designed for behavioral understanding. By continuously learning each organization’s normal patterns, it can detect deviations in real time, enabling a proactive and pre-emptive model of NDR where security teams can respond to early signs of risk as they emerge, reducing the window in which attacks can develop.

In multiple cases, this behavioral approach has led to early threat detection where Darktrace identified completely unknown threats, including pre-CVE zero-day activity. By detecting subtle behavioral changes before vulnerabilities were publicly disclosed or widely understood, organizations can mitigate threats before they do damage.

This shift is subtle but important. Modern NDR solutions must shift from a system that explains what happened to one that helps prevent threats from developing in the first place, and Darktrace is proud to be at the forefront of this shift - helping organizations build and maintain a state of proactive network resilience.

Continuing to innovate at the forefront of NDR

In our view, recognition as a Leader reflects where the market is today. Continuing to innovate defines what comes next.

As businesses evolve, new technologies like AI tools and agents introduce new security risks and challenges; security teams need more than simple detection. They need a complete understanding of risk as it develops, the ability to investigate it in context, and to contain threats at machine speed.  

Darktrace / NETWORK is built to deliver across that full spectrum. Its Self-Learning AI continuously adapts to each organization’s environment, identifying subtle behavioral changes that signal emerging threats. Integrated investigation and autonomous response reduce the time between detection and action, allowing teams to move with greater speed and confidence.

This combination enables organizations to detect and contain known, unknown, and insider threats as they develop, while also strengthening resilience over time.

As a two-time Leader in the Gartner® Magic Quadrant™ for NDR and the only 2025 Gartner® Peer Insights™ Customers’ Choice, we feel Darktrace continues to evolve its platform to meet the demands of modern environments, delivering a more complete and adaptive approach to network security.

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Disclaimer: The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR) ,The 2026 Gartner® Magic Quadrant™ for Network Detection and Response (NDR), Thomas Lintemuth, Charanpal Bhogal, Nahim Fazal, 18 May 2026.

Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved. Magic Quadrant is a registered trademark of Gartner, Inc. and/or its affiliates and is used herein with permission. All rights reserved.

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About the author
Mikey Anderson
Product Marketing Manager, Network Detection & Response

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May 21, 2026

Prompt Security in Enterprise AI: Strengths, Weaknesses, and Common Approaches

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How enterprise AI Agents are changing the risk landscape  

Generative AI Agents are changing the way work gets done inside enterprises, and subsequently how security risks may emerge. Organizations have quickly realized that providing these agents with wider access to tooling, internal information, and granting permissions for the agent to perform autonomous actions can greatly increase the efficiency of employee workflows.

Early deployments of Generative AI systems led many organizations to scope individual components as self-contained applications: a chat interface, a model, and a prompt, with guardrails placed at the boundary. Research from Gartner has shown that while the volume and scope of Agentic AI deployments in enterprise environments is rapidly accelerating, many of the mechanisms required to manage risk, trust, and cost are still maturing.

The issue now resides on whether an agent can be influenced, misdirected, or manipulated in ways that leads to unsafe behavior across a broader system.

Why prompt security matters in enterprise AI

Prompt security matters in enterprise AI because prompts are the primary way users and systems interact with Agentic AI models, making them one of the earliest and most visible indicators of how these systems are being used and where risk may emerge.

For security teams, prompt monitoring is a logical starting point for understanding enterprise AI usage, providing insight into what types of questions are being asked and tasks are being given to AI Agents, how these systems are being guided, and whether interactions align with expected behavior. Complete prompt security takes this one step further, filtering out or blocking sensitive or dangerous content to prevent risks like prompt injection and data leakage.

However, visibility only at the prompt layer can create a false sense of security. Prompts show what was asked, but not always why it was asked, or what downstream actions were triggered by the agent across connected systems, data sources, or applications.

What prompt security reveals  

The primary function of prompt security is to minimize risks associated with generative and agentic AI use, but monitoring and analysis of prompts can also grant insight into use cases for particular agents and model. With comprehensive prompt security, security teams should be able to answer the following questions for each prompt:

  • What task was the user attempting to complete?
  • What data was included in the request, and was any of the data high-risk or confidential?
  • Was the interaction high-risk, potentially malicious, or in violation of company policy?
  • Was the prompt anomalous (in comparison to previous prompts sent to the agent / model)?

Improving visibility at this layer is a necessary first step, allowing organizations to establish a baseline for how AI systems are being used and where potential risks may exist.  

Prompt security alone does not provide a complete view of risk. Further data is needed to understand how the prompt is interpreted, how context is applied, what autonomous actions the agent takes (if any), or what downstream systems are affected. Understanding the outcome of a query is just as important for complete prompt security as understanding the input prompt itself – for example, a perfectly normal, low-risk prompt may inadvertently result in an agent taking a high-risk action.

Comprehensive AI security systems like Darktrace / SECURE AI can monitor and analyze both the prompt submitted to a Generative AI system, as well as the responses and chain-of-thought of the system, providing greater insight into the behavior of the system. Darktrace / SECURE AI builds on the core Darktrace methodology, learning the expected behaviors of your organization and identifying deviations from the expected pattern of life.

How organizations address prompt security today

As prompt-level visibility has become a focus, a range of approaches have emerged to make this activity more observable and controllable. Various monitoring and logging tools aim to capture prompt inputs to be analyzed after the fact.  

Input validation and filtering systems attempt to intervene earlier, inspecting prompts before they reach the model. These controls look for known jailbreak patterns, language indicative of adversarial attacks, or ambiguous instructions which could push the system off course.

Importantly, for a prompt security solution to be accurate and effective, prompts must be continually observed and governed, rather than treated as a point-in-time snapshot.  

Where prompt security breaks down in real environments

In more complex environments, especially those involving multiple agents or extensive tool use, AI security becomes harder to define and control.

Agent-to-Agent communications can be harder to monitor and trace as these happen without direct user interaction. Communication between agents can create routes for potential context leakage between agents, unintentional privilege escalation, or even data leakage from a higher privileged agent to a lower privileged one.

Risk is shaped not just by what is asked, but by the conditions in which that prompt operates and the actions an agent takes. Controls at the orchestration layer are starting to reflect this reality. Techniques such as context isolation, scoped memory, and role-based boundaries aim to limit how far a prompt’s influence can extend.  

Furthermore, Shadow AI usage can be difficult to monitor. AI systems that are deployed outside of formal governance structures and Generative AI systems hosted on unknown endpoints can fly under the radar and can go unseen by monitoring tools, leaving a critical opening where adversarial prompts may go undetected. Darktrace / SECURE AI features comprehensive detection of Shadow AI usage, helping organizations identify potential risk areas.

How prompt security fits in a broader AI risk model

Prompt security is an important starting point, but it is not a complete security strategy. As AI systems become more integrated into enterprise environments, the risks extend to what resources the system can access, how it interprets context, and what actions it is allowed to take across connected tools and workflows.

This creates a gap between visibility and control. Prompt security alone allows security teams to observe prompt activity but falls short of creating a clear understanding of how that activity translates into real-world impact across the organization.

Closing that gap requires a broader approach, one that connects signals across human and AI agent identities, SaaS, cloud, and endpoint environments. It means understanding not just how an AI system is being used, but how that usage interacts with the rest of the digital estate.

Prompt security, in that sense, is less of a standalone solution and more of an entry point into a larger problem: securing AI across the enterprise as a whole.

Explore how Darktrace / SECURE AI brings prompt security to enterprises

Darktrace brings more than a decade of AI expertise, built on an enterprise‑wide platform designed to operate in and understand the behaviors of the complex, ambiguous environments where today’s AI now lives. With Darktrace / SECURE AI, enterprises can safely adopt, manage, monitor, and build AI within their business.  

Learn about Darktrace / SECURE AI here.

Sign up today to stay informed about innovations across securing AI.

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About the author
Jamie Bali
Technical Author (AI) Developer
Your data. Our AI.
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